Conference Agenda

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As the global sea level rises, there is increased concern about the growing urbanization of the low-lying deltaic coastal regions, and the related coastal hazards. Furthermore, the local relative sea-level rise can be significantly affected by vertical ground motions, either due to natural processes (e.g., global isostatic adjustment, tectonics, sediment consolidation and compaction, upstream sediment load reduction) and to human activities (e.g., groundwater extraction, land reclamation, building construction and consolidation). Deformation phenomena can be in the same order of magnitude (or greater) than climate-induced sea level rise. However, coastal ground motions are in practice often poorly known, and in many cases, little information is available about the patterns and time evolution of ground motion. It is, therefore, worth to monitor coastal delta regions through advanced Earth Observation (EO) systems that are capable detecting the ongoing surface deformation phenomena, recovering their spatial extent on the ground and following their temporal variability. This is beneficial for the subsequent interpretation of natural/anthropogenic processes causing surface motion.

The activities of this present Dragon IV project are mostly focused on the analysis of modification processes that characterize two important Delta river areas in China: the Yangtze and the Pearl River Deltas. Both delta regions are significantly affected by sea-level rise and natural/anthropogenic deformation phenomena, making it clear the need of extended analyses for a better understanding of the mechanisms responsible for the observed surface modifications, and for the planning of actions devoted to risk prevention for populations living in coastal areas.

More specifically, the aim is to retrieve long-term displacement time-series from EO data, specifically satellite Synthetic Aperture Radar (SAR), of the investigated areas through advanced differential interferometric synthetic aperture (DInSAR) techniques, as well as to complement DInSAR results with information derived from GPS/leveling campaigns with the aim to perform sound and extended geophysical analyses, and coastal erosion or accretion rates, which will be obtained by jointly exploiting archives of SAR data and the new generation of optical remote sensing systems.

In order to evaluate the combined risk of sea level rise, storm surges, and ground subsidence, the availability of high-resolution digital elevation models (DEM) of monitored coastal areas is mandatory. Added-value EO data products, such as the updated DEMs of coastal areas subject to sea-level rise, the time-series of terrain displacement, mean displacement velocity maps, and time-series of SAR backscattering maps, will be obtained by exploiting archives of SAR data with different levels of spatial resolution spanning a long time interval of about 20 years since the beginning of 2000s to 2020.

During the first year of this present D4 project, some preliminary activities have been conducted for processing Sentinel-1 data on both Delta regions by using different DInSAR tools in such a way to perform combined Small Baseline Subset (SBAS) [1] and Permanent Scatterers (PS) [2] analyses. Sentinel 1 time-series span about last three years. However, in order to have the historical perspective of the deformations occurring in the two areas, a few experiments have been carried out to obtain deformation maps covering the time lapse between 2002 to present days using (when available) ENVISAT and Cosmo-SkyMed SAR acquisitions. The latter are worth to complement the analyses performed over the last four years over the reclaimed lands of the Shanghai city [3].

The availability of updated DEMs of the areas subject to sea-level rise (and/or flooding) is also of fundamental importance. It is worth emphasizing that nowadays, global coverage catalogues of DEMs imaging most of the Earth’s surface are available (such as the ones recovered through the Shuttle Radar Topography Mission in 2001) and also new SAR missions have recently been specifically deployed (such as the TanDEM-X mission [4]) for the generation of updated profiles of the terrain. During the first months of activities, a DEM of the coastal area of the Shanghai megacity has been generated by applying the method [5]-[6] based on the use of bistatic SAR data acquired by the TerraSAR-X and TanDEM-X sensors. The analysis of the generated DEM, as well as its use for the application of flooding models in the Shanghai area, is still in progress.

The preliminarily results of the activities performed during the first year of this D4 project will be analyzed and properly integrated with the results of other investigations, for instance by performing SAR Tomography [] experimentations over Delta river cities.

Mean sea-level (MSL) is rising worldwide, and correlated changes in the ocean tides are also occurring; the combination of both may increase or diminish total sea-levels (TSL) in a complex manner. Higher ambient water levels can increase the flood risk to coastal zones, more so under storm surge episodes. However, even without a storm event, increasing MSL coupled with increasing tides may lead to more nuisance flooding, or flooding events produced from the coupling of high tides and rising MSL. Hong Kong is particularly sensitive to changes in water levels, as it has a large population with much of its infrastructure near the shore. Furthermore, land subsidence and past reclamation projects may have induced changes in resonant and frictional properties of the tides, which may yield non-uniform spatial changes in water levels throughout Hong Kong. In this work, the historical full-spectrum tidal variability at a number of coastal tide gauges in Hong Kong is determined, and the past behavior of the relations between major tides and shallow-water overtides is examined to understand the changes in friction and resonance and the implication for future water level spectra in the region. Additionally, the locations surrounding each tide gauge is examined via remote sensing images from the Sentinel series of earth observing satellites, and past and present images are contrasted to determine changes to coastal morphology, and compared to the frictional determinations of the ground based gauges and to the history of land subsidence and land reclamation in the region.

1Institute of Space and Earth Information System, The Chinese University of Hong Kong; 2School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

In recent years, quantitative measurements of significant wave height (SWH) by the use synthetic aperture radar (SAR) and radar cross section (RCS) methods has been proven effective in different approaches without prior knowledge of wind information. An update of an existing simple semi-empirical algorithm, aided by parameterized filtering of RCS, is presented in this study to address the issue of how the RCS values depend on empirical algorithms in different systems, using the wavelength value of the dominant wave peak and the relationship function of RCS to incidence angle derived from the SAR image. To determine the wavelength of dominant wave peaks, we implement a contrast limited adaptive histogram equalization (CLAHE) method, based on a parameterized image texture analysis. An adaptive filtering method ensures a statistically robust determination of the filtering parameter, resulting in a higher contrast SAR image that allows more efficient dominant wave peak identification, as clearer wave patterns can be revealed by higher image contrast level. We also propose a preliminary empirical update for the backscatter cross-section to incidence angle function for vertical polarization in a 5.405 GHz SAR system. Standard meteorological buoy data from National Buoy Data Center (NDBC) is used in development of the empirical model through validation. This research employs Level-1 GRD Sentinel-1A SAR images from 2015 to early 2017 that look at Hawaii and the central part of the west coast of the United States of America, selected to represent deep to shallow water depth and river influenced estuaries. However, extreme sea states are not considered due to the limitation of the image repository and buoy data availability. Beside the two analysis methods described above, additional detailed analyses are conducted on the sea state relation to the velocity bunching mechanism, based on SWH estimation result.